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A newer version of the Gradio SDK is available: 5.27.0

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metadata
title: Wetland Segmentation Deeplabsv3plus 🌿☘️
emoji: 💻
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 5.21.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: image segmentation

Wetlands Segmentation App

This Hugging Face Space provides an interactive web interface for segmenting wetland areas in satellite imagery using a DeepLabv3+ model.

Features

  • Upload satellite imagery in common formats (JPG, PNG) or GeoTIFF format
  • Optionally upload ground truth masks for evaluation
  • Visualize wetland segmentation predictions
  • Calculate metrics (IoU, Precision, Recall, F1) when ground truth is provided
  • View wetland coverage percentage statistics

Usage Instructions

  1. Upload Input Image:

    • Use the "Upload Image" tab for common image formats (JPG, PNG, etc.)
    • Use the "Upload TIFF" tab for GeoTIFF files with multiple bands

  2. Upload Ground Truth (Optional):

    • If you have a ground truth mask, upload it to see evaluation metrics
    • The ground truth should be a binary mask where white (255) represents wetlands

  3. Analyze:

    • Click the "Analyze Image" button to process the image
    • View the segmentation results and statistics

Model Information

This app uses a model from the dcrey7/wetlands_segmentation_deeplabsv3plus repository.

Model Architecture:

  • DeepLabv3+ with ResNet-50 backbone
  • Input: RGB satellite imagery (optimal size: 128×128 pixels)
  • Output: Binary segmentation mask (Wetland vs Background)

The model was trained on a dataset of satellite imagery containing wetland regions, focusing on environmental monitoring and conservation planning applications.

Example Output

When you upload an image, the app will display:

  • The original input image
  • The predicted wetland segmentation mask
  • Ground truth mask (if provided)
  • Statistics including wetland coverage percentage
  • Evaluation metrics (if ground truth is provided)

Limitations

  • The model works best on imagery similar to its training data
  • Performance may vary depending on image quality and characteristics
  • The model is designed for 128×128 pixel inputs (images will be resized)
  • While the model can process images with any number of bands, it was trained on RGB data

License

This application and the underlying model are available under the Apache 2.0 license.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference